Practical notes on
AI & systems
Field notes from delivering AI systems integration, enterprise automation, RAG, cloud and IoT — not marketing fluff.
Choosing Enterprise Workloads for Small Language Models
A practical framework for selecting small-model workloads based on scope, latency, cost, deployment constraints, and failure risk.
Validating AI Tool Calls Before They Reach Backend Systems
A practical, layered approach to validating AI-generated tool calls before they can affect enterprise systems.
Designing Safe Text-to-SQL for Enterprise Databases
Enterprise Text-to-SQL requires strict authorization, a governed semantic layer, deterministic validation, isolated execution, and auditable results.
Managing Context Budgets for Long Document Q and A
Reliable long-document Q and A depends on allocating context across chunking, summaries, retrieval, prompts, evidence, and the final response.
Regression Testing Before Updating Production AI Models
A practical framework for testing how model upgrades affect answer quality, integrations, cost, safety, and established production workflows.
Using Semantic Caching to Reduce AI Latency and Cost
A practical guide to cache boundaries, similarity thresholds, invalidation, and safe rollout for enterprise AI systems.
Combining Vision, Text, and Human Review for AI Inspection
A practical architecture for combining visual evidence, written specifications, operational data, and human review in AI inspection.
Designing OCR and LLM Pipelines for Complex Documents
A practical architecture for preserving document layout, extracting reliable fields, and routing uncertain results safely.
Building an Enterprise LLM Gateway for Quotas and Cost Control
A practical architecture for governing LLM access, quotas, cost attribution, and safe switching between model providers.
Verifying RAG Citations Against Source Documents
A practical engineering approach to document provenance, claim-level validation, conflict handling, and auditable RAG citations.
Alerting for Scheduled Publishing, Data Sync, and Batch Jobs: Monitor Delivery, Not Execution
A practical framework for defining failures, choosing signals, setting alert severity, and recovering scheduled workloads safely.
Executable Details to Include in Operations Handoff Documents
A useful operations handoff enables engineers to act, verify outcomes, contain risk, and recover without relying on the original delivery team.
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